The quality of your data impacts absolutely everything—from the effectiveness of the decisions your leadership team makes and the performance of your marketing campaigns, to the experience your customers have and the number of qualified leads generated, to the size and speed of deals closed, and profits won or lost. As far-reaching as it sounds, the importance of your data quality cannot be overstated. In short, it is the life’s blood of your company, and when it is tainted, it negatively impacts all systems that rely on it to function as they should. In today’s blog, we will cover the seven key components of high quality data that you need to account for with great regularity in order to ensure all systems are go.
4 Negative Impacts of Poor Data Quality
Because of data’s potential to do great wonders or great harm, many reputable companies, like IBM, Experian, SiriusDecisions and Gartner, have spent time, energy and a significant portion of their budgets to quantify its impact. Here are a few of their findings:
- Poor Data Quality Leads to Revenue Loss — Bad data costs businesses approximately $15 million per year in revenue loss.
- Poor Data Quality Leads to Customer Churn — 22% of businesses report that bad data caused them to lose customers.
- Poor Data Quality Leads to Poor-Performing Marketing Campaigns — 25% of the data inside the average B2B organization’s marketing database is inaccurate, which means at least a quarter of the money spent on campaigns is lost from the get-go.
- Poor Data Quality Leads to Compliance Risks — 80% of organizations will fail at least one GDPR compliance audit due to poor data management practices.
4 Benefits of High-Quality Data
When data is in an optimal state, it contributes to a multitude of positive outcomes with regards to productivity and profitability. Here are just a few:
- High Quality Data Improves operational efficiency – When all systems have high quality data to work with, it enables them to share information and insights with greater speed and efficiency.
- High Quality Data Reduces Costs – When more campaigns reach their intended targets with relevant information, it increases campaign ROI and customer satisfaction, and reduces the costs associated with creating and sending campaigns.
- High Quality Data Enables You to Be Proactive – When you have access to accurate data, you can better identify and mitigate risks, reduce negative outcomes, and respond faster to market changes.
- High Quality Data Allows you to Be More Competitive – When the data you are working with is clean and complete, you are able to make well-informed decisions with speed and precision.
7 Key Components of High-Quality Data
Maintaining high quality data requires vigilance, consistency, and a series of well-coordinated automated processes that monitor all data coming into or going out of your marketing technology ecosystem. When setting up your data monitoring processes, there are seven aspects of data quality that you will want to pay special attention to in order to ensure you and your teams are working with the best quality data possible, and they are:
- Accuracy — Is Your data correct?
It does not matter how robust and well-formatted your data is if it is not correct to begin with. To ensure the data you are working with is correct, you will need to implement data quality processes that include data profiling, cleansing, and validation. A couple of metrics that are commonly used to measure accuracy include:
- Error Rate — Percentage of inaccurate data in a dataset
- Validity Rate — Percentage of data that meets specific rules or constraints
2. Completeness — Is your data missing critical information?
Completeness refers to whether your data is missing critical information. To ensure data completeness, most businesses need to purchase data from a reputable third-party source in order to fill in all missing information. Metrics used to measure data completeness include:
- Completeness Rate — Percentage of complete data in the dataset
- Missing Values Rate — Percentage of missing values in the dataset
3. Validation — Is your data proven to be deliverable?
Validating your data not only ensures that it is reliable and accurate, but it makes certain that your campaigns will ultimately make it to their intended target. To validate your data, you will need to implement data validation rules that check your data against predefined rules and constraints. Metrics commonly used to measure validation include:
- Conformity Rate — Percentage of data that conforms to predefined rules and constraints
- Accuracy Rate — Percentage of accurate data in the dataset
4. Standardization — Is your data uniform across sources?
Standardization refers to whether the data is uniform across all of your data sources. Standardizing your data ensures that it is consistent and can be easily shared, compared and analyzed. To standardize your data you will need to implement data quality processes that set a standard for your data’s format and structure. Metrics used to measure standardization include:
- Consistency Rate — Percentage of data that is consistent across different sources
- Format Conformity Rate — Percentage of data that conforms to predefined data formats
5. Normalization — Is your data plagued By duplicates?
Normalization refers to whether or not your data is plagued by duplicates. Normalizing data ensures that there is only one unique record for each item, which helps to improve accuracy and reduces redundancy. To normalize your data, you should implement data quality processes that remove duplicates and merge records. Metrics used to measure normalization include:
- Duplicate Record Rate — Percentage of records that have duplicates
- Unique Record Rate — Percentage of records that are unique
6. Reliability — Are your data sources reliable?
Reliability refers to the trustworthiness of the data sources. To ensure your data is reliable make sure you select data sources carefully and monitor them regularly. Metrics used to measure reliability include:
Source Quality Rate — Percentage of data that comes from reliable sources
Source Accuracy Rate — Percentage of data from sources that are consistently accurate
7. Timeliness — Is your data current?
Timeliness refers to whether the data is current. Outdated data can lead to incorrect decisions, missed opportunities, and lost revenue. To ensure timeliness, make sure that the data that you collect is updated on a regular basis. Ideally you want your data to be updated in real-time before using it. Metrics used to measure timeliness include:
- Age of Data — The length of time since the data was last updated
- Frequency of Updates — How often the data is updated
Superior data quality is crucial to your organization’s ability to make informed decisions, build rewarding customer relationships and remain profitable. By implementing automated data quality processes and monitoring these metrics on a regular basis, you can ensure that the data populating your campaigns and informing your decisions is accurate, complete, validated, standardized, normalized, reliable, and timely.